Population structure of the European anchovy, Engraulis encrasicolus (Linnaeus, 1758) in Lake Manzala, Egypt

The present study is to identify the population and stock characteristics of Engraulis encrasicolus in the Mediterranean lagoon “Lake Manzala” of Egypt. A total of 1536 specimens were collected seasonally by a local trammel net (El-Balla), from 2019 to 2021. The length ranged from 4.2 to 12.1 cm, where the dominance was of medium sizes. Two age groups were observed with a short longevity (tmax = 3.16 y). Parameters of Von Bertalanffy, L?, and K, were estimated as 12.52 cm and 0.95 y-1, respectively. The growth performance index (Ø) was estimated as 2.17, expressing liner growth and environmental suitability. The calculated length at first maturity (Lm) = 8.1 cm, compared to 6.9 cm of length at first capture (Lc), expressing high fishing effort. Mortality indices include: total mortality (Z) = 3.71 y-1, and natural mortality (M) = 1.46 y-1. According to biological reference points, Fopt = 0.73 y-1 and Flimit = 0.97 y-1, the fishing mortality (F = 2.25 y-1) indicated overfishing of the anchovy stock in Lake Manzala. The current exploitation rate, E = 0.61 expressed the occurrence of overexploitation. Based on the results, reducing fishing efforts is vital to maintaining stock stability

An Enhanced Grey Wolf Optimizer with a Velocity-Aided Global Search Mechanism

This paper proposes a novel variant of the Grey Wolf Optimization (GWO) algorithm, named Velocity-Aided Grey Wolf Optimizer (VAGWO). The original GWO lacks a velocity term in its position-updating procedure, and this is the main factor weakening the exploration capability of this algorithm. In VAGWO, this term is carefully set and incorporated into the updating formula of the GWO. Furthermore, both the exploration and exploitation capabilities of the GWO are enhanced in VAGWO via stressing the enlargement of steps that each leading wolf takes towards the others in the early iterations while stressing the reduction in these steps when approaching the later iterations. The VAGWO is compared with a set of popular and newly proposed meta-heuristic optimization algorithms through its implementation on a set of 13 high-dimensional shifted standard benchmark functions as well as 10 complex composition functions derived from the CEC2017 test suite and three engineering problems. The complexity of the proposed algorithm is also evaluated against the original GWO. The results indicate that the VAGWO is a computationally efficient algorithm, generating highly accurate results when employed to optimize high-dimensional and complex problems

An Enhanced Grey Wolf Optimizer with a Velocity-Aided Global Search Mechanism

This paper proposes a novel variant of the Grey Wolf Optimization (GWO) algorithm, named Velocity-Aided Grey Wolf Optimizer (VAGWO). The original GWO lacks a velocity term in its position-updating procedure, and this is the main factor weakening the exploration capability of this algorithm. In VAGWO, this term is carefully set and incorporated into the updating formula of the GWO. Furthermore, both the exploration and exploitation capabilities of the GWO are enhanced in VAGWO via stressing the enlargement of steps that each leading wolf takes towards the others in the early iterations while stressing the reduction in these steps when approaching the later iterations. The VAGWO is compared with a set of popular and newly proposed meta-heuristic optimization algorithms through its implementation on a set of 13 high-dimensional shifted standard benchmark functions as well as 10 complex composition functions derived from the CEC2017 test suite and three engineering problems. The complexity of the proposed algorithm is also evaluated against the original GWO. The results indicate that the VAGWO is a computationally efficient algorithm, generating highly accurate results when employed to optimize high-dimensional and complex problems